Data center management method, management apparatus, and data center system

ABSTRACT

A data center management method executed by a computer that includes a first memory and a second memory, the method includes storing, in the second memory, a predicted value calculated based on measured values obtained as measurement data and differences each corresponding to the measurement data obtained a predetermined period ago; storing, in the first memory, the measurement data as the measured value; calculating, based on each of the stored measured values, an amount of change which is a difference between the measured value and the predicted value, and storing the calculated amount in the second memory; calculating first corrected prediction data based on the measured value currently measured and the measurement values previously measured; calculating second corrected prediction data based on previous amounts of change and the first corrected prediction data; and controlling the device using an operation amount calculated based on the second corrected prediction data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2017-100705, filed on May 22,2017, the entire contents of which are incorporated herein by reference.

FIELD

The embodiment discussed herein is related to a data center managementmethod, a management apparatus, and a data center system.

BACKGROUND

For example, in a data center where a large number of information andcommunication technology (ICT) devices are installed, an air conditioneris used to limit temperature rises in the room housing the ICT devices.

In this regard, it is easy to maintain a target environment when changesin measurement indices related to air conditioning (for example,temperature and humidity) are predicted and the air conditioner iscontrolled based on the prediction.

However, when the prediction intervals are too long and not suitable forthe speed at which a measurement index changes, a change in thepredicted value may lag behind a change in the measured value. Thus, theabove-described prediction of measurement indices is not accurate allthe time.

Japanese Laid-open Patent Publication No. 2015-90691 discloses that aprediction model based on a linear regression model is changed when adeviation of prediction data from actual result data exceeds athreshold. The above patent document also discloses that a correctedprediction model is used when a deviation of prediction data in ashorter period exceeds a threshold.

Further, Japanese Laid-open Patent Publication No. 9-259110 discussesdata fluctuation prediction and assignment of weights according toprediction accuracy to the past amounts of change in measurement values.For example, other related arts is as follows.

Ogawa. Masatoshi, Ogai. Harutoshi, “Application of Large-ScaleDatabase-Based Online Modeling to Plant State Long-Term Estimation”, Thetransactions of the Institute of Electrical Engineers of Japan. C, Vol.131 No. 4 pp. 718 to 721 (2011) (Non-patent Document 1)

Ushida. Shun, Kimura. Hidenori, “Identification and Control of NonlinearSystem Utilizing the Just-In-Time Modeling Technique”, Journal of theSociety of Instrument and Control Engineers, Vol. 44 No. 2 pp. 102 to106 (2005) (Non-patent Document 2)

Anders Stenman, “Just-in-Time Models with Applications to DynamicalSystem”, Linkoping Studies in Science and Technology, Thesis No. 601,March 1997 (Non-patent Document 3)

It is desirable to improve the accuracy of predicting measurementindices used in environment management of a data center.

SUMMARY

According to an aspect of the invention, a data center management methodexecuted by a computer that manages a data center and includes a firstmemory and a second memory, the first memory being configured to storemeasured values obtained as measurement data for a device in the datacenter, and differences each corresponding to the measurement dataobtained a predetermined period ago, the data center management methodincludes storing, in the second memory, a predicted value calculatedbased on the measurement data and the differences; storing, in the firstmemory, the measured measurement data as the measured value;calculating, based on each of the measured values stored in the firstmemory, an amount of change which is a difference between the measuredvalue and the predicted value, and storing the calculated amount ofchange in the second memory; calculating first corrected prediction databased on the measured value currently measured and the measurementvalues previously measured and stored in the first memory, the firstcorrected prediction data being data obtained by correction of thepredicted value; calculating second corrected prediction data based onprevious amounts of change and the first corrected prediction data, thesecond corrected prediction data being data obtained by correction ofthe first corrected prediction data; and controlling the device using anoperation amount calculated based on the second corrected predictiondata.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating example configurations of an ICT deviceroom and an air conditioner room in a data center;

FIG. 2 is a diagram illustrating examples of measurement data;

FIG. 3 is a diagram illustrating example configurations of an ICT deviceroom and an air conditioner room in a data center;

FIG. 4A is a diagram illustrating an outline of first correction;

FIG. 4B is a diagram illustrating an outline of first correction;

FIG. 5A is a diagram illustrating an outline of second correction;

FIG. 5B is a diagram illustrating an outline of second correction;

FIG. 6 is a diagram illustrating an example module configuration of anair conditioner controller;

FIG. 7 is a diagram illustrating a flowchart of main processing;

FIG. 8 is a diagram illustrating an example of a measured value table;

FIG. 9 is a diagram illustrating an example of a first difference table;

FIG. 10 is a diagram illustrating a flowchart of processing to calculatefirst differences;

FIG. 11 is a diagram illustrating a flowchart of processing to calculatepredicted values;

FIG. 12 is a diagram illustrating an example of proximity data;

FIG. 13 is a diagram illustrating an example of an original predictedvalue table;

FIG. 14 is a diagram illustrating an example of a second differencetable;

FIG. 15 is a diagram illustrating a flowchart of processing to calculatesecond differences;

FIG. 16 is a diagram illustrating a flowchart of processing to determinefirst approximate equations;

FIG. 17 is a diagram illustrating an example of a regression line of anoriginal predicted value;

FIG. 18 is a diagram illustrating an example of a first approximateequation table;

FIG. 19 is a diagram illustrating a flowchart of first correctionprocessing;

FIG. 20 is a diagram illustrating an example of a first predicted valuetable;

FIG. 21 is a diagram illustrating a flowchart of the main processing;

FIG. 22 is a diagram illustrating a flowchart of processing to determinesecond approximate equations;

FIG. 23 is a diagram illustrating an example of a regression line of asecond difference;

FIG. 24 is a diagram illustrating a flowchart of second correctionprocessing;

FIG. 25 is a diagram illustrating an example of a second predicted valuetable;

FIG. 26 is a diagram illustrating a flowchart of the main processing;and

FIG. 27 is a functional block diagram of a computer.

DESCRIPTION OF EMBODIMENT

FIG. 1 illustrates example configurations of an ICT device room and anair conditioner room in a data center. ICT devices are housed in racksand installed in the ICT device room. Each ICT device heats up when acentral processing unit (CPU), which is an arithmetic processing device,and a dual in-line memory module (DIMM), which is a storage device,operate. An air conditioner is used to cool the ICT devices down. Whenthe ICT devices are servers, the ICT device room may be called a serverroom.

Although FIG. 1 depicts only one air conditioner 103, more than one airconditioner 103 may be installed. The air conditioner 103 circulates airto adjust humidity and temperature. To this end, the air conditioner 103has a dehumidifying and humidifying device to adjust humidity and acooling and heating device to adjust temperature. The air conditioner103 has an air conditioning fan to send out air. Air sent out from theair conditioner 103 is sent into the ICT device room through an airsupply port.

Meanwhile, air inside the ICT device room is returned to the airconditioner room through an exhaust air port. The air returned to theair conditioner room is discharged outdoors through an exhaust air portin the air conditioner room. Part of the air returned to the airconditioner room is taken into the air conditioner 103 through a damper.

As illustrated in FIG. 1, various sensors 105 are installed at locationsinside and outside the data center. A sensor 105 a installed outdoorsmeasures the dry-bulb temperature of outside air. A sensor 105 b alsoinstalled outdoors measures the relative humidity of outside air. Asensor 105 c installed in the air conditioner 103 measures the powerconsumption of the air conditioner 103. A sensor 105 d also installed inthe air conditioner 103 acquires a value indicating the operation stateof the air conditioner 103. A sensor 105 e installed behind the racks inthe ICT device room measures the dry-bulb temperature behind the racks.A sensor 105 f also installed behind the racks measures the relativehumidity behind the racks. A sensor 105 g installed in front of theracks in the ICT device room measures the dry-bulb temperature in frontof the racks. A sensor 105 h also installed in front of the racksmeasures the relative humidity in front of the racks. A sensor 105 iinstalled in each ICT device measures the dry-bulb temperature insidethe ICT device. A sensor 105 j also installed in each ICT devicemeasures the relative humidity inside the ICT device. A sensor 105 kinstalled in each ICT device measures the power consumption of the ICTdevice. A sensor 105 l also installed in each ICT device acquires avalue indicating the operation state of the ICT device. Various othersensors 105 may additionally be used.

Based on data collected from these sensors 105, an air conditionercontroller 101 controls the air conditioner 103. Measurement datacollected by the air conditioner controller 101 are now described. FIG.2 illustrates examples of measurement data. The measurement data may becollected periodically. For example, the measurement data are collectedat 10-minute intervals. The example in FIG. 2 depicts temperature A at alocation a, power consumption B of a device b, humidity C at a locationc, frequency of rotation D of a fan in a device d, CPU use rate E of adevice e, and operating rate F of a compressor in a device f.Hereinafter, the temperature A at the location a may be referred to asan index A; the consumption power B of the device b, an index B; thehumidity C at the location c, an index C; the frequency of rotation D ofthe fan in the device d, an index D; the CPU use rate E of the device e,an index E; and the operating rate F of the compressor in the device f,an index F. Although omitted in FIG. 2, measurement data other thanthese indices are also collected.

To control the air conditioner 103, with respect to some of the indices,the air conditioner controller 101 predicts values to be measured in thefuture. In this example, the index A to the index F are targeted for theprediction. As to the prediction target indices, measurement values forthe next measurement are predicted.

The air conditioner controller 101 obtains adjustment amounts toincrease or decrease operation amounts set in the air conditioner 103based on predicted values, and converts the adjustment amounts tocontrol signals. The air conditioner controller 101 then transmits thecontrol signals to the air conditioner 103 and thereby controls the airconditioner 103.

As illustrated in FIG. 3, a certain ICT device in the ICT device roommay collect the measurement data and calculate predicted values. In theexample illustrated in FIG. 3, the ICT device that calculates predictedvalues sends operation amounts for the air conditioner 103 to aconverter. The converter obtains adjustment amounts for the operationamounts and transmits the adjustment amounts to the air conditionercontroller 101. The air conditioner controller 101 converts theadjustment amounts into control signals and transmits the controlsignals to the air conditioner 103.

The above-described air conditioner controller 101 or the ICT devicethat calculates predicted values predicts values to be measured in thefuture based for example on just-in-time (JIT) modeling. Details ofprediction processing based on JIT modeling are disclosed in Non-patentDocuments 1 to 3. A method other than JIT modeling may be used tocalculate predicted values.

Note that a calculated predicted value may contain error. For example,an index for the temperature inside an ICT device may changedrastically. For such an index that changes drastically, a change in apredicted value may lag behind a change in a measured value. Theembodiment corrects such error. Specifically, first correction andsecond correction are performed.

FIG. 4A illustrates an outline of the first correction. The upper graphdepicts measured values and predicted values of the index A. Thevertical axis represents measured values x_(A)[i] and predicted valuesy_(A)[i]. The horizontal axis represents a measurement time point i.

In the example illustrated in FIG. 4A, at the 916-th measurement timepoint, the air conditioner controller 101 acquires a measured valuex_(A)[916] of the index A and also calculates a predicted valuey_(A)[917] of the index A for the 917-th measurement. Thereafter, at the917-th measurement time point, the air conditioner controller 101acquires a measured value x_(A)[917] of the index A and also calculatesa predicted value y_(A)[918] of the index A for the 918-th measurement.Similarly, at the 918-th measurement time point, the air conditionercontroller 101 acquires a measured value x_(A)[918] of the index A andalso calculates a predicted value y_(A)[919] of the index A for the919-th measurement.

As illustrated in FIG. 4A, the predicted value y_(A)[917] is smallerthan the measured value x_(A)[917], and the predicted value y_(A)[918]is smaller than the measured value x_(A)[918]. In this example, it isconceivable that the predicted values y_(A) are smaller than themeasured values x_(A) due to a rapid rise of the index A.

The first correction uses regression analysis that samples pairs of themeasured value x_(A)[i] and the predicted value y_(A)[i] for the samemeasurement time point i and determines a first approximate equation foruse to calculate an approximation of the predicted value y_(A)[i] basedon the measured value x_(A)[i]. In FIG. 4A, the sampled pairs aresurrounded by solid lines.

Then, at the 918-th measurement time point for example, the originallypredicted value y_(A)[919] is converted to a corrected predicted valuey_(A) ^(<1>)[919] using a first correction equation. The firstcorrection equation is an equation in which the relation between thepredicted value y_(A)[i] and the measured value x_(A)[i] in the firstapproximate equation is applied to the predicted value y_(A)[919] beforethe first correction and the predicted value y_(A) ^(<1>)[919] after thefirst correction. Hereinbelow, an originally predicted value is calledan original predicted value, and a predicted value after the firstcorrection is called a first predicted value. The superscript number inangle brackets in the variable denotation of a first predicted valueindicates the number of corrections the value has undergone. In thisexample, <1> represents that the predicted value has been correctedonce. Detailed descriptions are given later of the first approximateequation and the first correction equation.

In an example illustrated in FIG. 4B, it is expected that the firstpredicted value y_(A) ^(<1>)[919] is larger than the original predictedvalue y_(A)[919] and is closer to the measured value x_(A)[919].

In the second correction, the first predicted value is furthercorrected. The second correction may not be complete with onecorrection. In other words, correction may be performed through two ormore stages. Using FIG. 5A, a description is given below of the firststage of the second correction.

The second correction obtains first differences concerning measuredvalues and second differences concerning predicted values and focuses onthe relations between the first differences and the second differences.In the first stage of correction, the first differences and the seconddifferences are obtained based on the past measurement which goes backfrom the current measurement by one measurement time point. In otherwords, the going-back count is 1 in the first stage of correction.

For example, a first difference R_(A1)[917] for the 917-th measurementis obtained by subtraction of the measured value x_(A)[916] from themeasured value x_(A)[917]. A second difference S_(A1)[917] for the917-th measurement is obtained by subtraction of the measured valuex_(A)[916] from the predicted value y_(A)[917]. Similarly, a firstdifference R_(A1)[918] for the 918-th measurement is obtained bysubtraction of the measured value x_(A)[917] from the measured valuex_(A)[918]. A second difference S_(A1)[918] for the 918-th measurementis obtained by subtraction of the measured value x_(A)[917] from thepredicted value y_(A)[918]. The subscript 1 in the variable denotationsof the first and second differences represents that the going-back countis 1.

The second correction uses regression analysis sampling pairs of thefirst difference R_(A1)[i] and the second difference S_(A1)[i] for thesame measurement time point and determines a second approximate equationfor use to calculate an approximation of the second difference S_(A1)[i]based on the first difference R_(A1)[i].

Then, at the 918-th measurement time point for example, the firstpredicted value y_(A) ^(<1>)[919] is converted to a second predictedvalue y_(A) ^(<2>)[919] with a going-back count of 1 using a secondcorrection equation. The second correction equation is an equation inwhich the relation between the predicted value y_(A)[i] and the measuredvalue x_(A)[i] in the second approximate equation is applied to thepredicted value y_(A) ^(<1>)[919] before the second correction and thepredicted value y_(A) ^(<2>)[919] after the second correction. Detaileddescriptions are given later of the second approximate equation and thesecond correction equation.

In an example illustrated in FIG. 5B, it is expected that the secondpredicted value y_(A) ^(<2>)[919] is larger than the first predictedvalue y_(A) ^(<1>)[919] and is further closer to the measured valuex_(A)[919].

If a predetermined condition is not met, the going-back count isincremented by one to perform the next stage of correction. If thepredetermined condition is met, the second correction is ended with thisstage. A detailed description of the second correction is given later.This is the end of the description of the outline of the presentembodiment.

Hereinbelow, the operation of the air conditioner controller 101 isdescribed in accordance with the configuration illustrated in FIG. 1.FIG. 6 illustrates an example module configuration of the airconditioner controller 101. The air conditioner controller 101 includesa wait part 601, a measurement part 603, a first calculation part 605, asecond calculation part 607, a third calculation part 609, a fourthcalculation part 611, a first determination part 613, a seconddetermination part 615, a first correction part 617, a second correctionpart 619, a conversion part 621, and a transmission part 623.

The wait part 601 performs processing to wait for measurement timing.The measurement part 603 acquires values measured by the sensors 105.The first calculation part 605 performs processing to calculatepredicted values. The processing to calculate predicted values isdescribed later using FIG. 11. The second calculation part 607 performsprocessing to calculate first differences. The processing to calculatefirst differences is described later using FIG. 10. The thirdcalculation part 609 performs processing to calculate seconddifferences. The processing to calculate second differences is describedlater using FIG. 15. The fourth calculation part 611 calculatesoperation amounts for the air conditioner 103 based on predicted valuesof the indices.

The first determination part 613 performs processing to determine firstapproximate equations. The processing to determine first approximateequations is described later using FIG. 16. The second determinationpart 615 performs processing to determine second approximate equations.The processing to determine second approximate equations is describedlater using FIG. 22. The first correction part 617 performs firstcorrection processing. The first correction processing is describedlater using FIG. 19. The second correction part 619 performs secondcorrection processing. The second correction processing is describedlater using FIG. 24.

The first determination part 613, the second determination part 615, thefirst correction part 617, and the second correction part 619 correspondto a corrected prediction data generation unit 620 that generatescorrected prediction data.

The conversion part 621 converts an adjustment amount for an operationamount set in the air conditioner 103 into a control signal. Thetransmission part 623 transmits a control signal to the air conditioner103.

The fourth calculation part 611, the conversion part 621, and thetransmission part 623 correspond to a control unit 624 that controls theair conditioner 103.

The air conditioner controller 101 further includes a measured valuestorage 631, an original predicted value storage 633, a first differencestorage 635, a second difference storage 637, a first approximateequation storage 639, a first predicted value storage 641, and a secondpredicted value storage 643.

The measured value storage 631 stores a measured value table. Themeasured value table is described using FIG. 8. The original predictedvalue storage 633 stores an original predicted value table. The originalpredicted value table is described later using FIG. 13. The firstdifference storage 635 stores a first difference table. The firstdifference table is described later using FIG. 9. The second differencestorage 637 stores a second difference table. The second differencetable is described later using FIG. 14. The first approximate equationstorage 639 stores a first approximate equation table. The firstapproximate equation table is described later using FIG. 18. The firstpredicted value storage 641 stores a first predicted value table. Thefirst predicted value table is described later using FIG. 20. The secondpredicted value storage 643 stores a second predicted value table. Thesecond predicted value table is described later using FIG. 25.

A table integrating the measured value table and the first differencetable may be stored in a measurement data storage 636. In this case, themeasurement data storage 636 integrally includes the measured valuestorage 631 and the first difference storage 635.

Further, a table integrating the original predicted value table and thesecond difference table may be stored in a prediction data storage 638.In this case, the prediction data storage 638 integrally includes theoriginal predicted value storage 633 and the second difference storage637.

The wait part 601, the measurement part 603, the first calculation part605, the second calculation part 607, the third calculation part 609,the fourth calculation part 611, the first determination part 613, thesecond determination part 615, the first correction part 617, the secondcorrection part 619, the conversion part 621, and the transmission part623 described above are implemented using hardware resources (forexample, FIG. 27) and programs that cause the processor to execute theprocessing to be described below.

The measured value storage 631, the original predicted value storage633, the first difference storage 635, the second difference storage637, the first approximate equation storage 639, the first predictedvalue storage 641, and the second predicted value storage 643 describedabove are implemented using hardware resources (for example, FIG. 27).

Next, a description is given of processing performed by the airconditioner controller 101. FIG. 7 illustrates a flowchart of the mainprocessing performed by the air conditioner controller 101. The waitpart 601 waits for measurement timing (S701). In this example,measurement is performed at certain intervals (for example, 10 minutes).

The measurement part 603 acquires values measured by the sensors 105,namely measured values, from the sensors 105 (S703). The measurementpart 603 stores the acquired measured values into the measured valuetable.

FIG. 8 illustrates an example of the measured value table. The measuredvalue table in this example has a record for each measurement timepoint. Each record in the measured value table has a field for storing ameasurement time point, a field for storing a measured value x_(A) ofthe temperature A at the location a, a field for storing a measuredvalue x_(B) of the consumption power B of the device b, and a field forstoring a measured value x_(C) of the temperature C at the location c.Each record of the measured value table also has a field for storing ameasured value x_(D) of the frequency of rotation D of the fan in thedevice d, a field for storing a measured value x_(E) of the CPU use rateE of the device e, a field for storing a measured value x_(F) of theoperating rate F of the compressor in the device f, and fields forstoring measured values x of other indices. In the measurement timepoint, “t” denotes the number of the measurement currently performed,namely, the number of the current measurement.

Referring back to the flowchart in FIG. 7, the second calculation part607 performs the processing to calculate first differences (S705). Thefirst differences calculated by the second calculation part 607 arestored into the first difference table.

FIG. 9 illustrates an example of the first difference table. The firstdifference table in this example has a record for each measurement timepoint. Each record in the first difference table has a field for storinga measurement time point, a field for storing first differences R_(A1)to R_(A5) of the index A with going-back counts of 1 to 5, respectively,and a field for storing first differences R_(B1) to R_(B5) of the indexB with going-back counts of 1 to 5, respectively.

Each record of the first difference table also has a field for storingfirst differences R_(C1) to R_(C5) of the index C with going-back countsof 1 to 5, respectively, a field for storing first differences R_(D1) toR_(D5) of the index D with going-back counts of 1 to 5, respectively, afield for storing first differences R_(E1) to R_(E5) of the index E withgoing-back counts of 1 to 5, respectively, and a field for storing firstdifferences R_(F1) to R_(F5) of the index F with going-back counts of 1to 5, respectively. FIG. 9, however, omits the fields for storing thefirst differences of the indices C to F.

FIG. 10 is a flowchart of the processing to calculate first differences.The second calculation part 607 selects one index N (S1001). In thisstep, an index other than the prediction indices (indices A to F) may beselected.

The second calculation part 607 sets 1 to a going-back count j which isan internal parameter (S1003). The second calculation part 607identifies a measured value x_(N)[t] of the selected index N for thecurrent measurement t (S1005). The second calculation part 607identifies a measured value x_(N)[t−j] of the selected index N for thepast measurement (t−j) (S1007). The second calculation part 607 thencalculates a first difference R_(Nj)[t] (S1009). Specifically, thesecond calculation part 607 obtains the first difference R_(Nj)[t] bysubtracting the measured value x_(N)[t−j] of the selected index N forthe past measurement (t−j) from the measured value x_(N)[t] of theselected index N for the current measurement t.

The second calculation part 607 stores the first difference R_(Nj)[t]into the record for the current measurement t in the first differencetable (S1011). The second calculation part 607 determines whether thegoing-back count j is equal to a predetermined value (S1013). Thepredetermined value is an envisaged upper limit of the going-back count(in this example, five).

If it is determined that the going-back count j is not equal to thepredetermined value, the second calculation part 607 increments thegoing-back count j by one (S1015). The flowchart then proceeds back tothe processing in S1005 and repeats the above processing.

If it is conversely determined that the going-back count j is equal tothe predetermined value, the second calculation part 607 determineswhether there is any unselected index N (S1017).

If it is determined that there is any unselected index N, the flowchartproceeds back to the processing in S1001 and repeats the aboveprocessing. If it is conversely determined that there is no unselectedindex N, the processing to calculate first differences is ended, and theflowchart returns to the calling main processing.

Referring back to the flowchart in FIG. 7, the first calculation part605 performs the processing to calculate predicted values (S707).

FIG. 11 illustrates a flowchart of the processing to calculate predictedvalues. In the processing to calculate predicted values, predictedvalues are calculated based for example on the above-described JITmodeling. The processing to calculate predicted values is a conventionaltechnique, and is therefore described only briefly herein. The firstcalculation part 605 selects one prediction index N (S1101). In thisexample, one of the indices A to F is selected.

Based on the measured values stored in all the items for eachmeasurement time point in the measured value table and the firstdifference stored in all the items for each measurement time point inthe first difference table, the first calculation part 605 identifies anassociated index number highly correlated with the prediction index(S1103). The associated index number is identified by an index type anda going-back count. For example, when it is determined that the index Bthat goes back four measurement time points and the index C that goesback six measurement time points are highly correlated with the index A,the index B with a going-back count of 4 and the index C with agoing-back count of 6 are associated indices for the prediction index A.In this example, the first difference may serve as the associated indexnumber.

The first calculation part 605 determines the value of the associatedindex number to be inputted to the prediction model (hereinafterreferred to as an input value) (S1105). To obtain a predicted value forthe next measurement (t+1), the input value is the measured value of anassociated index for the measurement corresponding to the going-backcount of the associated index minus 1. For example, an input value as tothe associated index B with a going-back count of 4 is a measured value_(XB)[t−3] of the index B obtained three measurement time points ago.

The first calculation part 605 identifies proximity data (S1107). FIG.12 illustrates an example of the proximity data. In the exampledescribed above, out of the measured values of the associated index Bwith a going-back count of 4, ones whose difference from the input valueis, in absolute value, equal to or below a predetermined value areextracted as samples. In FIG. 12, the points located between the twovertical lines are the samples.

Referring back to the flowchart in FIG. 11, the first calculation part605 generates a prediction model based on the extracted samples (S1109).Specifically, the first calculation part 605 finds a regression lineequation using regression analysis. Then, the regression lines for therespective associated indices are combined to obtain a calculationequation corresponding to a prediction model. FIG. 12 illustrates anexample of a regression line.

In the above example where the index B with a going-back count of 4 andthe index C with a going-back count of 6 are the associated indices forthe prediction index A, the calculation equation isy_(A)[t+1]=Q_(A)[t]+P_(A,B)[t]×x_(B)[t−3]+P_(A,C)[t]×x_(C)[t−5], wherey_(A)[t+1] is a predicted value of the prediction index A for the nextmeasurement (t+1), Q_(A)[t] is a constant for the prediction of theprediction index A for the current measurement t, P_(A,B)[t] is acoefficient of the associated index B for the prediction of theprediction index A for the current measurement t, x_(B)[t−3] is ameasured value of the index B obtained three measurements ago (thisvalue corresponds to the input value), P_(A,C)[t] is a coefficient ofthe associated index C for the prediction of the prediction index A forthe current measurement t, and x_(C)[t−5] is a measured value of theindex C obtained five measurements ago (this value corresponds to theinput value).

The first calculation part 605 applies the input values to theabove-described prediction model and thereby calculates a predictedvalue (S1111). A predicted value obtained using the prediction model ishereinafter referred to as an original predicted value. The firstcalculation part 605 stores an original predicted value y_(N)[t+1] intothe record for the next measurement (t+1) in the original predictedvalue table (S1113).

FIG. 13 illustrates an example of the original predicted value table.The original predicted value table in this example has a record for eachmeasurement time point. Each record of the original predicted valuetable has a field for storing a measurement time point, a field forstoring an original predicted value y_(A) of the temperature A at thelocation a, a field for storing an original predicted value y_(B) of thepower consumption B of the device b, and a field for storing an originalpredicted value y_(C) of the temperature C at the location c. Eachrecord of the original predicted value table also has a field forstoring an original predicted value y_(D) of the frequency of rotation Dof the fan in the device d, a field for storing an original predictedvalue y_(E) of the CPU use rate E of the device e, a field for storingan original predicted value y_(F) of the operation rate F of thecompressor in the device f.

Referring back to the flowchart in FIG. 11, the first calculation part605 determines whether there is any unselected prediction index N(S1115). If it is determined that there is any unselected predictedindex N, the flowchart proceeds back to the processing in S1101 andrepeats the above processing. If it is conversely determined that thereis no unselected prediction index N, the processing to calculatepredicted values is ended, and the flowchart returns to the calling mainprocessing.

Referring back to the flowchart in FIG. 7, the third calculation part609 performs the processing to calculate second differences (S709). Thesecond differences calculated by the third calculation part 609 arestored into the second difference table.

FIG. 14 illustrates an example of the second difference table. Thesecond difference table in this example has a record for eachmeasurement time point. Each record of the second difference table has afield for storing a measurement time point, fields for storing seconddifferences S_(A1) to S_(A5) of the index A with going-back counts of 1to 5, respectively, and fields for storing second differences S_(B1) toS_(B5) of the index B with going-back counts of 1 to 5, respectively.

Each record of the second difference table also has fields for storingsecond differences S_(C1) to S_(C5) of the index C with going-backcounts of 1 to 5, respectively, fields for storing second differencesS_(D1) to S_(D5) of the index D with going-back counts of 1 to 5,respectively, fields for storing second differences S_(E1) to S_(E5) ofthe index E with going-back counts of 1 to 5, respectively, and fieldsfor storing second differences S_(F1) to S_(F5) of the index F withgoing-back counts of 1 to 5. However, FIG. 14 omits the fields forstoring second differences for the indices C to F.

FIG. 15 illustrates a flowchart of the processing to calculate seconddifferences. The third calculation part 609 selects one prediction indexN (S1501).

The third calculation part 609 sets 1 to the going-back count j which isan internal parameter (S1503). The third calculation part 609 identifiesan original predicted value y_(N)[t+1] of the selected index N for thenext measurement (t+1) (S1505). The third calculation part 609identifies a measured value x_(N)[t+1−j] of the selected index N for thepast measurement (t+1−j) (S1507). The third calculation part 609 thencalculates a second difference S_(N)[t+1] (S1509). Specifically, thethird calculation part 609 obtains the second difference S_(Nj)[t+1] bysubtracting the measured value x_(N)[t+1−j] of the selected index N forthe past measurement (t+1-j) from the original predicted valuey_(N)[t+1] of the selected index N for the next measurement (t+1).

The third calculation part 609 stores the second difference S_(Nj)[t+1]into the record for the next measurement (t+1) in the second differencetable (S1511). The third calculation part 609 determines whether thegoing-back count j is equal to a predetermined value (S1513). Thepredetermined value is an envisaged upper limit of the going-back count(in this example, five).

If it is determined that the going-back count j is not equal to thepredetermined value, the third calculation part 609 increments thegoing-back count j by one (S1515). The third calculation part 609 thenproceeds back to the processing in S1505 and repeats the aboveprocessing.

If it is conversely determined that the going-back count j is equal tothe predetermined value, the third calculation part 609 determineswhether there is any unselected index N (S1517).

If it is determined that there is any unselected index N, the thirdcalculation part 609 proceeds back to the processing in S1501 andrepeats the above processing. If it is conversely determined that thereis no unselected index N, the third calculation part 609 ends theprocessing to calculate second differences and returns to the callingmain processing.

Referring back to the flowchart in FIG. 7, the first determination part613 performs the processing to determine first approximate equations(S711).

FIG. 16 illustrates a flowchart of the processing to determine firstapproximate equations. The first determination part 613 selects oneprediction index N (S1601).

As samples, the first determination part 613 extracts pairs of themeasured value x_(N)[i] and the original predicted value y_(N)[i] withina range where, for example, the measurement time point i is 1 to t(S1603).

Using linear regression that uses the extracted samples, the firstdetermination part 613 calculates a coefficient α_(N) and a constantβ_(N) for a regression equation for a predicted value (S1605). Theregression equation for a predicted value is an example of a firstapproximate equation for finding an approximation of the originalpredicted value y_(N)[i] based on the measured value x_(N)[i]. Theregression equation for a predicted value isy_(N)[i]=α_(N)×x_(N)[i]+β_(N).

FIG. 17 illustrates an example of a regression line of an originalprediction value. The example illustrated in FIG. 17 concerns theprediction index A. The horizontal axis represents the measured valuex_(A)[i] of the prediction index A, and the vertical axis represents theoriginal predicted value y_(A)[i] of the prediction index A. The pointsindicated with a cross are the samples. The straight line in FIG. 17 isa regression line expressed by a regression equation for a predictedvalue.

Referring back to the flowchart in FIG. 16, the first determination part613 stores the coefficient α_(N) and the constant β_(N) for theregression equation for a predicted value into the record for theselected index N in the first approximate equation table (S1607).

FIG. 18 illustrates an example of the first approximate equation table.The first approximate equation table in this example has a record foreach prediction index. Each record of the first approximate equationtable has a field for storing the coefficient α_(N) and a field forstoring the constant β_(N).

Referring back to the flowchart in FIG. 16. The first determination part613 determines whether there is any unselected index N (S1609). If it isdetermined that there is any unselected prediction index N, theflowchart proceeds back to the processing in S1601 and repeats the aboveprocessing. If it is determined conversely that there is no unselectedprediction index N, the processing to determine first approximateequations is ended, and the flowchart returns to the calling mainprocessing.

Referring back to the flowchart in FIG. 7, the first correction part 617performs the first correction processing (S713).

FIG. 19 illustrates a flowchart of the first correction processing. Thefirst correction part 617 selects one prediction index N (S1901).

The first correction part 617 calculates a first predicted value y_(N)^(<1>)[t+1] by applying the original predicted value y_(N)[t+1] to afirst correction equation (S1903). The first correction equation isy_(N) ^(<1>)[t+1]=(y_(N)[t+1]−β_(N))/α_(N). The first correctionequation is equivalent to an equation which is based on the firstapproximate equation and replaces the predicted value y_(N)[i] and themeasured value x_(N)[i] in the first approximate equation with theoriginal predicted value y_(N)[t+1] and the first predicted value y_(N)^(<1>)[t+1], respectively. Thus, the first predicted value y_(N)^(<1>)[t+1] is closer to the measured value x_(N)[t+1] obtained in thenext measurement (t+1).

The first correction part 617 stores the first predicted value y_(N)^(<I>)[t+1] into the record for the next measurement (t+1) in the firstpredicted value table (S1905).

FIG. 20 illustrates an example of the first predicted value table. Thefirst predicted value table in this example has a record for eachmeasurement time point. Each record of the first predicted value tablehas a field for storing a measurement time point, a field for storing afirst predicted value y_(A) ^(<1>) of the temperature A at the locationa, a field for storing a first predicted value y_(B) ^(<1>) of the powerconsumption B of the device b, and a field for storing a first predictedvalue y_(C) ^(<1>) of the humidity C at the location c. Each record ofthe first predicted value table further has a field for storing a firstpredicted value y_(D) ^(<1>) of the frequency of rotation D of the fanin the device d, a field for storing a first predicted value y_(E)^(<1>) of the CPU use rate E of the device e, and a field for storing afirst predicted value y_(F) ^(<1>) of the operating rate F of thecompressor in the device f.

Referring back to the flowchart in FIG. 19, the first correction part617 determines whether there is any unselected prediction index N(S1907). If it is determined that there is any unselected predictionindex N, the flowchart proceeds back to the processing in S1901 andrepeats the above processing. If it is conversely determined that thereis no unselected prediction index N, the first correction processing isended, and the flowchart returns to the calling main processing.

Referring back to the flowchart in FIG. 7, after S713 of the firstcorrection processing, the flowchart proceeds to processing in S2101illustrated in FIG. 21 via terminal A.

Proceeding to the main processing illustrated in FIG. 21, the seconddetermination part 615 selects one prediction index N (S2101). Thesecond determination part 615 sets 1 to the going-back count j which isan internal parameter (S2103).

The second determination part 615 performs processing to determinesecond approximate equations (S2105).

FIG. 22 illustrates a flowchart of the processing to determine secondapproximate equations. The second determination part 615 extracts pairsof the first difference R_(Nj)[i] and the second difference S_(Nj)[i]within a range where, for example, the measurement time point i is j+1to t (S2201).

Using linear regression that uses the extracted samples, the seconddetermination part 615 calculates a coefficient α_(Nj) and a constantβ_(Nj) for a regression equation for a second difference (S2203). Theregression equation for a second difference is an example of a secondapproximate equation for finding an approximation of the seconddifference S_(Nj)[i] based on the first difference R_(Nj)[i]. Theregression equation for a second difference isS_(Nj)[i]=α_(Nj)×R_(Nj)[i]+β_(Nj).

FIG. 23 illustrates an example of a regression line for a seconddifference. The example in FIG. 23 concerns the prediction index A. Thehorizontal axis represents the first difference R_(A1)[i] with agoing-back count of 1, and the vertical axis represents the seconddifference S_(A1)[i] with a going-back count of 1. The points indicatedwith a cross are the samples. The straight line in FIG. 23 is aregression line expressed by the regression equation for a seconddifference.

Referring back to the flowchart in FIG. 22, the second determinationpart 615 retains the coefficient α_(Nj) and the constant β_(Nj) for theregression equation for a second difference as internal parameters(S2205). After the processing to determine second approximate equationsends, the flowchart returns to the calling main processing.

Referring back to the flowchart in FIG. 21, the second correction part619 performs second correction processing (S2107).

FIG. 24 illustrates a flowchart of the second correction processing. Thesecond correction part 619 calculates a (j+1)-th predicted value y_(N)^(<j>)+1>[t+1] by applying a j-th predicted value y_(N) ^(<j>)[t+1] andthe measured value x_(N)[t] to a second correction equation (S2401).

For example, in the second correction processing for the first time(j=1), the second correction part 619 calculates the second predictedvalue y_(N) ^(<2>)[t+1] by applying the first predicted value y_(N)^(<j>)[t+1] and the measured value x_(N)[t] to a second correctionequation: y_(N) ^(<2>)[t+1]={y_(N)^(<1>)[t+1]+(α_(N1)−1)×x_(N)[t]−β_(N1)}/α_(N1).

The second correction equation used in the second correction processingfor the first time (j=1) is equivalent to an equation which is based onthe definition of the first difference, the definition of the seconddifference, and the second approximate equation and which replaces theoriginal predicted value y_(N)[i], the measured value x_(N)[i], and themeasured value x_(N)[i−1] with the j-th predicted value y_(N)^(<1>)[t+1], the (j+1)-th predicted value y_(N) ^(<2>)[t+1], and themeasured value x_(N)[t], respectively.

A general second correction equation is y_(N) ^(<j+1>)[t+1]={y_(N)^(<j>)[t+1]+(α_(Nj)−1)×x_(N)[t]−β_(Nj)}/α_(Nj). This equation isequivalent to an equation which is based on the definition of the firstdifference, the definition of the second difference, and the secondapproximate equation and which replaces the original predicted valuey_(N)[i], the measured value x_(N)[i], and the measured value x_(N)[i−j]with the j-th predicted value y_(N) ^(<j>)[t+1], the (j+1)-th predictedvalue y_(N) ^(<j+1>)[t+1], and the measured value x_(N)[t+1−j],respectively.

The second correction part 619 stores the (j+1)-th predicted value y_(N)^(<J+1>)[t+1] into the record for the next measurement (t+1) in thesecond predicted value table (S2403).

FIG. 25 illustrates an example of the second predicted value table. Thesecond predicted value table in this example has a record for eachmeasurement time point. Each record of the second predicted value tablehas a field for storing a measurement time point, a field for storing asecond predicted value y_(A) ^(<2>) of the temperature A at the locationa, a field for storing a second predicted value y_(B) ^(<2>) of thepower consumption B of the device b, and a field for storing a secondpredicted value y_(C) ^(<2>) of the humidity C at the location c. Eachrecord of the second predicted value table further has a field forstoring a second predicted value y_(D) ^(<2>) of the frequency ofrotation D of the fan in the device d, a field for storing a secondpredicted value y_(E) ^(<2>) of the CPU use rate E of the device e, anda field for storing a second predicted value y_(F) ^(<2>) of theoperating rate F of the compressor in the device f.

Referring back to the flowchart in FIG. 21, the second determinationpart 615 determines whether the coefficient α_(N) for the going-backcount j exceeds the coefficient α_(N) for the going-back count j−1(S2109). In this example, when the coefficient α_(N) for the going-backcount j exceeds the coefficient α_(N) for the going-back count j−1, itis determined that correction of the predicted value is complete, andthe iteration is ended. Alternatively, the iteration may be ended whenthe going-back count j reaches a predetermined value.

If it is determined that the coefficient α_(N) for the going-back countj does not exceed the coefficient α_(N) for the going-back count j−1,the second determination part 615 increments the going-back count j byone (S2111). Then, the flowchart proceeds back to the processing inS2105 and repeats the above processing. If it is conversely determinedthat the coefficient α_(N) for the going-back count j exceeds thecoefficient α_(N) for the going-back count j−1, the second determinationpart 615 determines whether there is any unselected prediction index N(S2113).

If it is determined that there is any unselected prediction index N, theflowchart proceeds back to the processing in S2101 and repeats the aboveprocessing. If it is conversely determined that there is no unselectedprediction index N, the flowchart proceeds to the processing in S2601illustrated in FIG. 26 via terminal B.

Control of the air conditioner 103 based on the predicted values for theindices is performed with a conventional technique, and is thereforedescribed only briefly herein. The fourth calculation part 611calculates operation amounts for the air conditioner 103 based on thefinal predicted values obtained for the respective predicted indices(S2601). The fourth calculation part 611 calculates adjustment amountsfor the operation amounts based on the operation amounts currently setin the air conditioner 103 (S2603). The conversion part 621 converts theadjustment amounts for the operation amounts into control signals(S2605). The transmission part 623 transmits the control signals to theair conditioner 103 (S2607). The air conditioner 103 operates accordingto the control signals. The flowchart then returns to the processing inS701 of FIG. 7 via terminal C.

According to the present embodiment, prediction accuracy may be improvedfor measurement indices used in the environmental management of a datacenter.

According to the present embodiment, accuracy of correcting predictedvalues may be improved using regression analysis.

The second correction may be omitted, and only the first correction maybe performed. Conversely, the first correction may be omitted, and onlythe second correction may be performed.

Although the embodiment of the disclosure has been described, thedisclosure is not limited to the embodiment. For example, the functionalblock configuration described above may not be consistent with a programmodule configuration.

The above-described structure of the storage areas is merely an example,and the disclosure is not limited thereto. Further, the processes in theflowcharts may be changed in order, or two or more processes may beperformed in parallel, as long as it does not change the processingoutcome.

The air conditioner controller 101 described above is a computer devicein which, as illustrated in FIG. 27, a memory 2501, a CPU 2503, a harddisk drive (HDD) 2505, a display controller 2507 connected to a displaydevice 2509, a drive device 2513 for a removable disk 2511, an inputdevice 2515, and a communication controller 2517 for connecting to anetwork are connected to one another via a bus 2519. An operating system(OS) and application programs for implementing the processing in theembodiment are stored in the HDD 2505, and are loaded from the HDD 2505into the memory 2501 when they are to be executed by the CPU 2503. TheCPU 2503 controls the display controller 2507, the communicationcontroller 2517, and the drive device 2513 according to the processingdetails selected by the application programs and causes them to performpredetermined operations. Data currently being processed is storedmainly in the memory 2501, but may be stored in the HDD 2505. In theembodiment of the disclosure, the application programs for implementingthe above-described processing are stored in the computer-readableremovable disk 2511, distributed, and installed from the drive device2513 to the HDD 2505. The application programs may be installed into theHDD 2505 via a network, such as the Internet, and the communicationcontroller 2517. Such a computer device implements the various functionsdescribed above when hardware such as the CPU 2503 and the memory 2501organically cooperate with programs such as the OS and the applicationprograms.

The embodiment of the disclosure described above may be summed up asfollows.

The management apparatus according to the embodiment is a data centermanagement apparatus that manages a data center and comprises: (A) ameasurement data storage that stores measured data obtained asmeasurement data for a device in the data center and difference dataeach concerning the measurement data obtained a predetermined periodago; (B) a predicted data calculation part that calculates predicteddata based on the measurement data and the difference data and storesthe predicted data into a prediction data storage; (C) a measurementpart that stores the measured measurement data into the measurement datastorage as the above-mentioned measured data; (D) an amount of changecalculation part that, based on each measured data stored in themeasurement data storage, calculates an amount of change which is thedifference between the measured data and the predicted data and storesthe calculated amount of change into the predicted data storage; (E) acorrected prediction data generation part that calculates firstcorrected prediction data (predicted data obtained by correction of thepredicted data) based on the current measured data and the previousmeasured data stored in the measurement data storage, and calculatessecond corrected prediction data (predicted data obtained by correctionof the first predicted data) based on the previous amounts of change andthe first corrected prediction data; and (F) a control part thatcontrols the device using an operation amount calculated based on thecorrected prediction data.

With such a configuration, prediction accuracy may be improved for themeasurement indices used in the environmental management of the datacenter.

The measurement data storage 636 illustrated in FIG. 6 is an example ofthe above measurement data storage. The prediction data storage 638illustrated in FIG. 6 is an example of the above prediction datastorage. The first calculation part 605 illustrated in FIG. 6 is anexample of the above predicted data calculation part. The measurementpart 603 illustrated in FIG. 6 is an example of the above the abovemeasurement part. The third calculation part 609 illustrated in FIG. 6is an example of the above amount of change calculation part. Thecorrected prediction data generation unit 620 illustrated in FIG. 6 isan example of the above corrected prediction data generation part. Thecontrol unit 624 illustrated in FIG. 6 is an example of the abovecontrol part.

Further, the corrected prediction data generation part may calculate thefirst corrected prediction data based on the current measurement dataand the slope and intercept of a first approximate line expressing theprevious measured data stored in the measurement data storage.

In this way, the accuracy of correcting predicted values may be improvedbased on the first approximate line expressing the previous measureddata.

Further, the corrected prediction data generation part may calculate asecond slope and a second intercept of a second approximate lineexpressing previous amounts of change and calculate the second correctedprediction data based on the first corrected prediction data, eachamount of change calculated based on the previous amounts of change, andthe second slope and the second intercept.

In this way, the accuracy of correcting predicted values may be improvedbased on the second approximate line expressing the previous amounts ofchange.

Programs for causing a computer to execute the above-describedprocessing in the management apparatus may be created and stored in, forexample, a computer-readable storage medium or a storage device such asa flexible disk, a CD-ROM, a magneto-optical disk, a semiconductormemory, or a hard disk. Typically, intermediate processing results aretemporarily stored in a storage device such as main memory.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiment of the presentinvention has been described in detail, it should be understood that thevarious changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A data center management method executed by acomputer that manages a data center and includes a first memory and asecond memory, the first memory being configured to store measuredvalues obtained as measurement data for a device in the data center, anddifferences each corresponding to the measurement data obtained apredetermined period ago, the data center management method comprising:storing, in the second memory, a predicted value calculated based on themeasurement data and the differences; storing, in the first memory, themeasured measurement data as the measured value; calculating, based oneach of the measured values stored in the first memory, an amount ofchange which is a difference between the measured value and thepredicted value, and storing the calculated amount of change in thesecond memory; calculating first corrected prediction data based on themeasured value currently measured and the measurement values previouslymeasured and stored in the first memory, the first corrected predictiondata being data obtained by correction of the predicted value;calculating second corrected prediction data based on previous amountsof change and the first corrected prediction data, the second correctedprediction data being data obtained by correction of the first correctedprediction data; and controlling the device using an operation amountcalculated based on the second corrected prediction data.
 2. The datacenter management method according claim 1, wherein the calculatingfirst corrected prediction data includes calculating the first correctedprediction data based on the current measurement data and a slope and anintercept of a first approximate line expressing the previous measuredvalues stored in the first memory.
 3. The data center management methodaccording claim 1, wherein the calculating second corrected predictiondata includes: calculating a second slope and a second intercept of asecond approximate line expressing the previous amounts of change, andcalculating the second corrected prediction data based on the firstcorrected prediction data, each change amount calculated based on theprevious amounts of change, the second slope, and the second intercept.4. The data center management method according claim 1, wherein thecalculating first corrected prediction data includes by use ofregression analysis that samples pairs of the measured values and thepredicted values for respective measurement time points, determining afirst approximate equation that calculates an approximation of thepredicted value based on the measured value for each of the measurementtime points, determining a first correction equation in which a relationbetween the measured value and the predicted value for the measurementtime point in the first approximate equation is applied to the predictedvalue before correction and the predicted value after correction, andcalculating the first corrected prediction data using the firstcorrection equation determined.
 5. The data center management methodaccording claim 4, wherein the calculating second corrected predictiondata includes: by use of regression analysis that samples pairs of firstdifferences and second differences for the respective measurement timepoints, the first differences each indicating a difference between themeasured values and the second differences each indicating a differencebetween the predicted value and the measured value, determining a secondapproximate equation that calculates an approximation of the seconddifference based on the first difference, determining a secondcorrection equation in which a relation between the measured value andthe predicted value in the second approximate equation is applied to thepredicted value before correction and the predicted value aftercorrection, and calculating the second corrected prediction data usingthe second correction equation determined.
 6. The data center managementmethod according claim 1, wherein the measurement data includes at leastone of a temperature at a first location, a power consumption of a firstdevice, a humidity at a second location, a frequency of rotation of afan in a second device, a use rate of a processor in a third device, andan operating rate of a compressor in a fourth device.
 7. The data centermanagement method according claim 1, wherein the predicted value iscalculated using a just-in-time (JIT) modeling method.
 8. The datacenter management method according claim 1, wherein the device is an airconditioner.
 9. The data center management method according claim 7,wherein the controlling includes: calculating an adjustment amount forthe operating amount based on the operating amount, converting thecalculated adjustment amount into a control signal, and transmitting theconverted control signal to the device.
 10. A management apparatus thatmanages a data center, the management apparatus comprising: a firstmemory that stores measured values obtained as measurement data for adevice in the data center, and differences each corresponding to themeasurement data obtained a predetermined period ago; a second memory;and a processor connected to the first memory and the second memory andconfigured to: store, in the second memory, a predicted value calculatedbased on the measurement data and the differences; store, in the firstmemory, the measured measurement data as the measured value; calculate,based on each of the measured values stored in the first memory, anamount of change which is a difference between the measured value andthe predicted value, and store the calculated amount of change in thesecond memory; calculate first corrected prediction data based on themeasured value currently measured and the measurement values previouslymeasured and stored in the first memory, the first corrected predictiondata being data obtained by correction of the predicted value; calculatesecond corrected prediction data based on previous amounts of change andthe first corrected prediction data, the second corrected predictiondata being data obtained by correction of the first corrected predictiondata; and control the device using an operation amount calculated basedon the second corrected prediction data.
 11. A data center systemcomprising: a data center; and a management apparatus that manages thedata center, wherein the management apparatus includes: a first memorythat stores measured values obtained as measurement data for a device inthe data center, and differences each corresponding to the measurementdata obtained a predetermined period ago; a second memory; and aprocessor connected to the first memory and the second memory andconfigured to: store, in the second memory, a predicted value calculatedbased on the measurement data and the differences, store, in the firstmemory, the measured measurement data as the measured value, calculate,based on each of the measured values stored in the first memory, anamount of change which is a difference between the measured value andthe predicted value, and store the calculated amount of change in thesecond memory, calculate first corrected prediction data based on thecurrent measured value currently measured and measurement valuespreviously measured and stored in the first memory, the first correctedprediction data being data obtained by correction of the predictedvalue, calculate second corrected prediction data based on previousamounts of change and the first corrected prediction data, the secondcorrected prediction data being data obtained by correction of the firstcorrected prediction data, and control the device using an operationamount calculated based on the second corrected prediction data.